Development and clinical application of an integrative genomic approach to personalized cancer therapy

Andrew V Uzilov1, Wei Ding1, Marc Y Fink1,2

  • 1Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.

Genome Medicine
|June 2, 2016
PubMed
Abstract

Insights

A comprehensive genomic approach significantly improves personalized cancer therapy (PCT) by identifying more actionable alterations than standard methods. This integrative strategy enhances treatment predictions and outcomes for cancer patients.

Area of Science:

  • Oncology
  • Genomics
  • Precision Medicine

Background:

  • Personalized therapy offers optimal cancer care outcomes.
  • Advancements in cancer research, tumor profiling, and targeted therapeutics facilitate clinical implementation.
  • Integrative genomics is key to understanding tumor complexity.

Purpose of the Study:

  • To develop and evaluate a personalized cancer therapy (PCT) program using an integrative genomics approach.
  • To characterize tumor complexity through comprehensive genomic profiling.
  • To identify somatic mutations, copy number alterations, gene expression changes, gene fusions, and germline variants.

Main Methods:

  • Whole exome sequencing (WES) and SNP microarray genotyping of tumor and normal specimens.
  • RNA sequencing (RNA-Seq) for gene expression and fusion analysis.
  • Ion AmpliSeq Cancer Hotspot Panel v2 (CHPv2) for mutation hotspots, integrated with knowledge bases and cancer-type specific workflows.

Main Results:

  • Identified a mean of 17.3 cancer-relevant somatic mutations per patient, significantly more than targeted panels.
  • Found actionable alterations in 91% of patients (mean 4.9 per patient), including mutations, CNAs, gene expression changes, and germline variants.
  • Genomic findings altered treatment course in four cases, demonstrating clinical utility.

Conclusions:

  • Comprehensive, integrative genomic profiling significantly enhances genomics-based PCT strategies.
  • This approach provides a deeper understanding of tumor drivers and improves therapeutic predictions.
  • The study highlights the value of multi-omic data integration for personalized cancer treatment.

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